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Research Square|April 1, 2025
The R.O.A.D. to clinical trial emulationDimitris Bertsimas, Angelos Koulouras, Hiroshi Nagata, et al.
Journal of Law and the Biosciences|May 2, 2022
Ethics-by-design: efficient, fair and inclusive resource allocation using machine learningTheodore P Papalexopoulos, Dimitris Bertsimas, I Glenn Cohen, et al.
World Journal for Pediatric & Congenital Heart Surgery|November 16, 2021
Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification TreesDimitris Bertsimas, Daisy Zhuo, Jordan Levine, et al.
Advances in Health Sciences Education : Theory and Practice|October 19, 2002
Using Error/Tolerance Analysis to Design an Empirical Practice AnalysisMichael Kane
JCO Clinical Cancer Informatics|January 18, 2019
Applied Informatics Decision Support Tool for Mortality Predictions in Patients With CancerDimitris Bertsimas, Jack Dunn, Colin Pawlowski, et al.
The Lancet. Digital Health|July 27, 2026
Identifying subsets of patients with retroperitoneal sarcoma who benefit from radiotherapy: an Interpretable AI reanalysis of the STRASS randomised trialDimitris Bertsimas, Georgios Antonios Margonis, Angelos Koulouras, et al.
Pharmacoeconomics|December 13, 2017
Sensitivity of the Medication Possession Ratio to Modelling Decisions in Large Claims DatabasesMargret V Bjarnadottir, David Czerwinski, Eberechukwu Onukwugha
The Annals of Thoracic Surgery|December 8, 2023
Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking ToolGeorge E Sarris, Daisy Zhuo, Luca Mingardi, et al.
World Journal for Pediatric & Congenital Heart Surgery|April 28, 2021
Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning ApproachDimitris Bertsimas, Daisy Zhuo, Jack Dunn, et al.
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